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Music Recommendation: Audio Neighbourhoods to Discover Music in the Long Tail

  • Susan Craw
  • Ben Horsburgh
  • Stewart Massie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9343)

Abstract

Millions of people use online music services every day and recommender systems are essential to browse these music collections. Users are looking for high quality recommendations, but also want to discover tracks and artists that they do not already know, newly released tracks, and the more niche music found in the ‘long tail’ of on-line music. Tag-based recommenders are not effective in this ‘long tail’ because relatively few people are listening to these tracks and so tagging tends to be sparse. However, similarity neighbourhoods in audio space can provide additional tag knowledge that is useful to augment sparse tagging. A new recommender exploits the combined knowledge, from audio and tagging, using a hybrid representation that extends the track’s tag-based representation by adding semantic knowledge extracted from the tags of similar music tracks. A user evaluation and a larger experiment using Last.fm user data both show that the new hybrid recommender provides better quality recommendations than using only tags, together with a higher level of discovery of unknown and niche music. This approach of augmenting the representation for items that have missing information, with corresponding information from similar items in a complementary space, offers opportunities beyond content-based music recommendation.

Keywords

Recommender systems Novelty and serendipity Knowledge extraction CBR similarity assumption 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computing Science and Digital MediaRobert Gordon UniversityAberdeenUK

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